Transcription factor and microRNA are two types of key regulators of gene expression. Their regulatory mechanisms are highly complex. In this study, we propose a computational method to predict condition-speci c regulatory modules that consist of microRNAs, transcription factors, and their commonly regulated genes. We used matched global expression pro les of mRNAs and microRNAs together with the predicted targets of transcription factors and microRNAs to construct an underlying regulatory network. Our method searches for highly scored modules from the network based on a two-step heuristic method that combines genetic and local search algorithms. Using two matched expression datasets, we demonstrate that our method can identify highly scored modules with statistical signi cance and biological relevance. e identi ed regulatory modules may provide useful insights on the mechanisms of transcription factors and microRNAs. ; Copyright © 2013 Wenbo Mu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Scientific World, Scientific World Journal ; The research was partially supported by the Chancellor’s Discovery Fund, University of Illinois at Chicago.